A straightforward nonlinear extension of Granger's concept of causality in the kernel framework is suggested. The kernel-based approach to assessing nonlinear Granger causality in multivariate time series enables us to determine, in a model-free way, whether the causal relation between two time series is present or not and whether it is direct or mediated by other processes. The trace norm of the so-called covariance operator in feature space is used to measure the prediction error. Relying on this measure, we test the improvement of predictability between time series by subsampling-based multiple testing. The distributional properties of the resulting p-values reveal the direction of Granger causality. Experiments with simulated and real-world data show that our method provides encouraging results. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Sun, X. (2008). Assessing nonlinear granger causality from multivariate time series. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5212 LNAI, pp. 440–455). https://doi.org/10.1007/978-3-540-87481-2_29
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